1 citations · 1 across the 7 of their papers we have counts for
8 papers
SimTrace: Grounded Multimodal User Trajectories Generation for Online User Modeling
Yunan Lu, Shuang Xie, Meghna Allamudi +4
Virtual clients offer a cost-effective approach to support applications such as A/B testing, recommender system development, and interface evaluation. However, building them requir…
EARS: Explanatory Abstention for Reliable Sub-Agent Modeling in Large-scale Multi-Agent Systems
Shuang Xie, Yunan Lu, Han Li +1
In large-scale enterprise settings, centralized multi-agent systems (MAS) are increasingly adopted, in which a coordinator delegates user requests to lightweight, domain-specialize…
VISTA: A Versatile Interactive User Simulation Toolkit for Agent Evaluation
Yunan Lu, Ryan Shea, Yusen Zhang +1
Evaluation remains a critical bottleneck for interactive agent development. Existing evaluation methods often rely on static benchmarks, which fail to capture the dynamic, multi-st…
MortarBench: Evaluating Mortgage Loan Origination Agents
Matthew Toles, Yunan Lu, Manav Munjal +6
Loan origination is the process by which a lender creates a new loan, from application and underwriting through approval and funding. This process serves a critical role in evaluat…
SAGE: A Top-Down Bottom-Up Knowledge-Grounded User Simulator for Multi-turn AGent Evaluation
Ryan Shea, Yunan Lu, Liang Qiu +1
Evaluating multi-turn interactive agents is challenging due to the need for human assessment. Evaluation with simulated users has been introduced as an alternative, however existin…
Speculative Actions: A Lossless Framework for Faster Agentic Systems
Naimeng Ye, Arnav Ahuja, Georgios Liargkovas +3
AI agents are increasingly deployed in complex, interactive environments, yet their runtime remains a major bottleneck for training, evaluation, and real-world use. Typical agent b…
LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems
Xiao Yu, Yunan Lu, Zhou Yu
Retrieval-augmented question-answering systems combine retrieval techniques with large language models to provide answers that are more accurate and informative. Many existing tool…
Mixture of Soft Prompts for Controllable Data Generation
Derek Chen, Celine Lee, Yunan Lu +2
Large language models (LLMs) effectively generate fluent text when the target output follows natural language patterns. However, structured prediction tasks confine the output form…